WASABI: a dynamic iterative framework for gene regulatory network inference.

BACKGROUND: Inference of gene regulatory networks from gene expression data has been a long-standing and notoriously difficult task in systems biology. Recently, single-cell transcriptomic data have been massively used for gene regulatory network inference, with both successes and limitations. RESULTS: In the present work we propose an iterative algorithm called WASABI, dedicated to inferring a causal dynamical network from time-stamped single-cell data, which tackles some of the limitations associated with current approaches. We first introduce the concept of waves, which posits that the information provided by an external stimulus will affect genes one-by-one through a cascade, like waves spreading through a network. This concept allows us to infer the network one gene at a time, after genes have been ordered regarding their time of regulation. We then demonstrate the ability of WASABI to correctly infer small networks, which have been simulated in silico using a mechanistic model consisting of coupled piecewise-deterministic Markov processes for the proper description of gene expression at the single-cell level. We finally apply WASABI on in vitro generated data on an avian model of erythroid differentiation. The structure of the resulting gene regulatory network sheds a new light on the molecular mechanisms controlling this process. In particular, we find no evidence for hub genes and a much more distributed network structure than expected. Interestingly, we find that a majority of genes are under the direct control of the differentiation-inducing stimulus. CONCLUSIONS: Together, these results demonstrate WASABI versatility and ability to tackle some general gene regulatory networks inference issues. It is our hope that WASABI will prove useful in helping biologists to fully exploit the power of time-stamped single-cell data.

Capturing the mutational landscape of the beta-lactamase TEM-1.

Adaptation proceeds through the selection of mutations. The distribution of mutant fitness effect and the forces shaping this distribution are therefore keys to predict the evolutionary fate of organisms and their constituents such as enzymes. Here, by producing and sequencing a comprehensive collection of 10,000 mutants, we explore the mutational landscape of one enzyme involved in the spread of antibiotic resistance, the beta-lactamase TEM-1. We measured mutation impact on the enzyme activity through the estimation of amoxicillin minimum inhibitory concentration on a subset of 990 mutants carrying a unique missense mutation, representing 64% of possible amino acid changes in that protein reachable by point mutation. We established that mutation type, solvent accessibility of residues, and the predicted effect of mutations on protein stability primarily determined alone or in combination changes in minimum inhibitory concentration of mutants. Moreover, we were able to capture the drastic modification of the mutational landscape induced by a single stabilizing point mutation (M182T) by a simple model of protein stability. This work thereby provides an integrated framework to study mutation effects and a tool to understand/define better the epistatic interactions.

Fitness, stress resistance, and extraintestinal virulence in Escherichia coli.

The extraintestinal virulence of Escherichia coli is dependent on numerous virulence genes. However, there is growing evidence for a role of the metabolic properties and stress responses of strains in pathogenesis. We assessed the respective roles of these factors in strain virulence by developing phenotypic assays for measuring in vitro individual and competitive fitness and the general stress response, which we applied to 82 commensal and extraintestinal pathogenic E. coli strains previously tested in a mouse model of sepsis. Individual fitness properties, in terms of maximum growth rates in various media (Luria-Bertani broth with and without iron chelator, minimal medium supplemented with gluconate, and human urine) and competitive fitness properties, estimated as the mean relative growth rate per generation in mixed cultures with a reference fluorescent E. coli strain, were highly diverse between strains. The activity of the main general stress response regulator, RpoS, as determined by iodine staining of the colonies, H2O2 resistance, and rpoS sequencing, was also highly variable. No correlation between strain fitness and stress resistance and virulence in the mouse model was found, except that the maximum growth rate in urine was higher for virulent strains. Multivariate analysis showed that the number of virulence factors was the only independent factor explaining the virulence in mice. At the species level, growth capacity and stress resistance are heterogeneous properties that do not contribute significantly to the intrinsic virulence of the strains.

Selection for chaperone-like mediated genetic robustness at low mutation rate: impact of drift, epistasis and complexity.

Genetic robustness is defined as the constancy of a phenotype in the face of deleterious mutations. Overexpression of chaperones, to assist the folding of proteins carrying deleterious mutations, is so far one of the most accepted molecular mechanisms enhancing genetic robustness. Most theories on the evolution of robustness have focused on the implications of high mutation rate. Here we show that genetic drift, which is modulated by population size, organism complexity, and epistasis, can be a sufficient force to select for chaperone-mediated genetic robustness. Using an exact analytical solution, we also show that selection for costly genetic robustness leads to a paradox: the decrease of population fitness on long timescales and the long-term dependency on robustness mechanisms. We suggest that selection for genetic robustness could be universal and not restricted to high mutation rate organisms such as RNA viruses. The evolution of the endosymbiont Buchnera illustrates this selection mechanism and its paradox: the increased dependency on chaperones mediating genetic robustness. Our model explains why most chaperones might have become essential even in optimal growth conditions.

The evolution of epistasis and its links with genetic robustness, complexity and drift in a phenotypic model of adaptation.

The epistatic interactions among mutations have a large effect on the evolution of populations. In this article we provide a formalism under which epistatic interactions among pairs of mutations have a distribution whose mean can be modulated. We find that the mean epistasis is correlated to the effect of mutations or genetic robustness, which suggests that such formalism is in good agreement with most in silico models of evolution where the same pattern is observed. We further show that the evolution of epistasis is highly dependant on the intensity of drift and of how complex the organisms are, and that either positive or negative epistasis could be selected for, depending on the balance between the efficiency of selection and the intensity of drift.